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View stack_overflow_test_program.c
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
void IShouldNeverBeCalled() {
puts("I should never be called");
void vulnerable(char *arg) {
import json
class PostDetais(object):
def __init__(self, soup, link=None):
self.page_soup = soup = link
def get_title(self):
class_names = ['graf graf--h3 graf-after--figure graf--title',
View function_pointer.c
// Function Pointer
#include <stdio.h>
int sum(int a, int b)
return a+b;
void hello_name(char *name)
View bin-sh-using-mprotect.c
// source:
#include <string.h>
#include <sys/mman.h>
// /bin/sh shellcode
const char shellcode[] = "\x01\x30\x8f\xe2\x13\xff\x2f\xe1\x03\xa0\x52\x40\xc2\x71\x05\xb4\x69\x46\x0b\x27\x01\xdf\x2d\x1c\x2f\x62\x69\x6e\x2f\x73\x68\x58";
int main(int argc, char **argv)
# Helps Visualizing overall summary of all features in a dataset
# !pip install numpy
# !pip install pandas
# !pip install autoviz
# !pip install xlrd
# !pip install xgboost
from autoviz.AutoViz_Class import AutoViz_Class
AV = AutoViz_Class()
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import classification_report, confusion_matrix
import joblib
import os
def preprocess(dataset, x_iloc_list, y_iloc, testSize):
# Modified from source:
# Feature Selection with Univariate Statistical Tests
from pandas import read_csv
from numpy import set_printoptions
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import f_classif
from sklearn.feature_selection import chi2